With the rise of big data, cloud computing, and self-service business intelligence tools, many organizations are questioning whether master data management (MDM) is still a relevant methodology. In this article, we’ll examine the key benefits and use cases for MDM in the modern data landscape.
What is master data management?
Master data management is a set of processes, governance, policies, standards, and tools that consistently define and manage critical business entities such as customers, products, suppliers, etc. The key goals of MDM are:
- Create a “single version of the truth” for key business data entities
- Improve data quality by eliminating duplicates, errors, and inconsistencies
- Standardize, enrich, and link master data across disparate systems
- Provide a central hub for trusted master data that can be leveraged across the organization
Proper MDM allows companies to gain greater value from their data assets, enabling business users to make decisions based on accurate information. The MDM processes include:
- Data standardization – Formatting data in a consistent manner
- Data deduplication – Identifying and removing duplicate data records
- Data enrichment – Augmenting data by adding missing attributes or external data
- Data governance – Managing policies, roles, workflows for master data
- Data distribution – Sharing master data across systems and applications
Is master data management still needed with modern data architectures?
With the shift towards big data architectures, cloud platforms, and self-service analytics, some question whether traditional MDM is still necessary. The counter-argument is that MDM is just as critical, if not more so, when managing and deriving value from complex, large-scale data environments. Some key reasons why MDM remains relevant include:
- Data lakes still need data governance – When implementing data lakes and big data platforms like Hadoop, organizations still need MDM to govern how master data is defined, organized, and made available to business users.
- Cloud doesn’t negate the need for data quality – Storing data in the cloud does not automatically ensure it is of high quality. MDM provides critical data quality, deduplication, and enrichment capabilities.
- Self-service BI relies on trusted data – For business self-service analytics tools to be effective, they need a dependable foundation of master data that has been properly defined, standardized, and integrated.
- MDM meshes with data mesh principles – The data mesh promotes decentralized data ownership and reuse. MDM principles and tools can help mesh-based architectures manage and share distributed domains.
While MDM originated in on-premise environments, it has adapted to enable more flexible, hybrid, and cloud-based data ecosystems. When properly implemented, MDM complements modern data platforms rather than being displaced by them.
Use cases showing MDM’s continued relevance
Here are some examples of how organizations continue to gain value from MDM initiatives:
Customer 360-degree view
Having an accurate, holistic view of customers and prospects remains critical in sales, marketing, and service. MDM provides a cleansed, unified customer profile by consolidating data from CRM, e-commerce, support, and other systems.
Product information management
Managing product data is hugely complex, especially for global enterprises. MDM helps standardize product attributes, classifications, pricing data, and relationships across channels and business units.
Supplier data integration
Organizations need consistent information on suppliers for procurement, logistics, and other functions. MDM helps integrate and maintain clean supplier data across finance, manufacturing, and fulfillment systems.
Internet of Things (IoT) enablement
The huge volume and velocity of IoT sensor data can overwhelm organizations. MDM provides governance for streaming device data by defining master hierarchies, taxonomies, metadata models, and data policies.
Reference data standardization
Codes, identifiers, and lookup values are used across applications but are often fragmented. MDM can standardize reference data like location codes, product numbers, and other domains.
Analytics foundation
For analytics and business intelligence to provide value, they need to be built on trusted, high-quality data. MDM provides the master data foundation for more accurate analytics and reporting.
Key criteria for a modern MDM approach
Given the continued need for MDM with contemporary data architectures, what capabilities should organizations look for in a modern master data management approach?
| Criteria | Description |
|---|---|
| Agility | Flexible and fast deployment options including cloud, on-premises, or hybrid. Out-of-the-box solutions to accelerate time-to-value. |
| Scalability | Ability to handle massive data volumes, velocity, variety, and complexity. Leverage big data platforms. |
| Data quality | Robust data quality tools for standardization, deduplication, and enrichment. Integration of data quality functions. |
| Semantics | Machine learning and semantic technologies to provide context and meaning to master data. |
| Self-service | Intuitive self-service interfaces and automation for business users to manage master data domains. |
| Governance | Model-driven governance of master data lifecycles, workflows, and business rules. |
A modern, agile MDM platform needs to leverage the scale and intelligence of emerging technologies while staying focused on delivering trusted data to drive business value.
Conclusion
Master data management remains a crucial discipline despite evolving data architectures and analytics landscapes. Organizations still need the core capabilities of MDM – unified master data, data quality, semantics, and governance – to maximize the business value of their data. While MDM solutions have adapted to new technologies like cloud platforms and big data, the fundamental goals and benefits of MDM remain highly relevant.
Rather than being displaced by self-service analytics and decentralized data, MDM is evolving to complement these trends and provide the master data foundation to fuel data-driven business initiatives. By selecting solutions designed for the new breed of hybrid and cloud-based data environments, organizations can continue to leverage MDM to drive value from data in the digital age.